Data Storage Durability via Hardware Failure Risk Indicators
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Solution Overview
Problem
Data storage systems face challenges in achieving high durability while minimizing resource usage and cost, particularly when dealing with heterogeneous data storage elements of varying reliability, as traditional replication methods do not effectively account for hardware reliability, leading to suboptimal resource utilization and uneven data loss probabilities.
Innovation Solution
The method involves determining hardware failure risk indicators for each data storage element and dynamically adjusting the quantity and distribution of replicas based on these indicators to optimize data durability, ensuring a more uniform loss probability distribution and resource usage across the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional replication methods are used to store multiple replicas of data objects, then data durability is improved, but resource usage increases and cost increases
Solution Approach 1:
The patent applies local quality by assigning different replica counts to different data storage elements based on their individual hardware reliability indicators. Instead of uniformly replicating data across all storage elements, the system dynamically determines the number of replicas for each element, placing more replicas on high-reliability elements and fewer or no replicas on low-reliability elements. This resolves the contradiction by optimizing resource usage while maintaining data durability through localized, intelligence-based replication strategies.
2Reliability
If traditional replication methods are used to store multiple replicas of data objects, then data durability is improved, but cost increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the replica count parameter based on hardware reliability indicators. The system monitors hardware conditions and adjusts the number of replicas stored on each data storage element accordingly. When hardware reliability is high, more replicas are maintained; when reliability decreases, replica counts are reduced. This resolves the contradiction by making replication costs adaptive to actual hardware conditions, reducing unnecessary replication costs while maintaining adequate data durability.
3Productivity
If replicas are distributed across heterogeneous data storage elements of varying reliability, then resource utilization is improved, but data loss probability becomes uneven across the system
Solution Approach 1:
The patent applies local quality by making replica distribution decisions element-specific based on individual hardware reliability indicators. Each data storage element receives a customized replica allocation according to its reliability profile, ensuring that elements with higher reliability contribute more to data redundancy while elements with lower reliability store fewer or no replicas. This resolves the contradiction by achieving both efficient resource utilization and uniform data loss probability across the system through localized, intelligence-based allocation.
Solution Approach 2:
The patent applies feedback by continuously monitoring hardware reliability indicators and using this information to dynamically adjust replica distribution. The system observes hardware conditions, processes this information through reliability modeling, and adjusts replication strategies accordingly. This closed-loop feedback mechanism ensures that resource utilization is optimized while maintaining uniform data loss probability, as the system adapts to changing hardware conditions in real-time.
Data Source
AI summary
Methods, apparatuses, systems, and devices are described for improving data durability in a data storage system. In one example method of improving data durability, a hardware failure risk indicator may be determined for each of a plurality of data storage elements in the data storage system. The method may also include storing one or more replicas of a first data object on one or more of the plurality of data storage elements, with a quantity of the one or more replicas and a distribution of the one or more replicas among the plurality of data storage elements being a function of the hardware failure risk indicators for each of the plurality of data storage elements. In some examples, the hardware failure risk indicators may be dynamically updated based on monitored conditions, which may result in dynamic adjustments to the quantity and distribution of the data object replicas.